Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)
| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot)
plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2)
tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F)
print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.8533e+08 2 5.0572 0.0066 **
## lag_depvar 2.6311e+11 1 2700.8304 <2e-16 ***
## Residuals 8.1637e+10 838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1815.288 16272.96 0.1461049
## 2-0 31342.464 23204.768 39480.16 0.0000000
## 2-1 24113.626 19404.275 28822.98 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
## 42 19319.29 1 30103.29
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## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
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## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
## 838 49195.00 2 59230.71
## 839 55436.43 2 49195.00
## 840 50353.14 2 55436.43
## 841 43194.86 2 50353.14
## 842 47539.71 2 43194.86
## 843 35271.00 2 47539.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 686 53576.73 22058.674
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2019.947747 4040.826857 -538.488471 2437.710411 -2970.455559
## 7 8 9 10 11
## 518.446613 -5656.288601 -1187.200519 -3965.463927 -416.675962
## 12 13 14 15 16
## -4938.595890 -1607.516361 -897.854591 379.247892 -3241.458596
## 17 18 19 20 21
## -376.073673 -2128.486855 6605.915719 -1529.219056 -1208.125594
## 22 23 24 25 26
## 1475.890499 -1186.788041 234.617340 1694.831899 -7102.625817
## 27 28 29 30 31
## 948.516757 8193.073990 417.191599 -14.925416 -2401.363918
## 32 33 34 35 36
## 1576.033665 4572.066707 1125.651912 2390.166770 -1869.542993
## 37 38 39 40 41
## 4607.013462 4303.218335 -2276.731517 -2981.838886 -1109.868781
## 42 43 44 45 46
## -10740.973967 7291.968168 2558.013477 1366.971958 8104.992001
## 47 48 49 50 51
## 684.346346 6527.056732 6711.587939 -5886.139165 -4797.524603
## 52 53 54 55 56
## -5060.537936 -7928.209141 6131.775004 -4076.170118 -4893.106552
## 57 58 59 60 61
## 3857.869116 888.807889 -31.472340 142.773337 -4996.001263
## 62 63 64 65 66
## 18128.179579 3638.125572 -3649.048561 5923.607883 7341.367324
## 67 68 69 70 71
## 14635.151281 1687.076973 -13218.257927 -1308.031038 4642.270355
## 72 73 74 75 76
## -4902.239695 -4405.075205 -10496.823519 2469.983064 -5397.348850
## 77 78 79 80 81
## 1067.301198 -6863.257311 551.310714 -2350.716064 -2689.791873
## 82 83 84 85 86
## -3927.526358 -532.623797 2319.151141 3766.146173 479.005654
## 87 88 89 90 91
## -482.866074 198.160573 4303.211892 -1162.973214 1151.153214
## 92 93 94 95 96
## -2064.494103 -1043.810623 178.263627 275.335250 -7483.623518
## 97 98 99 100 101
## 2394.072963 -8600.993723 -2936.863366 -4035.191768 -1731.640747
## 102 103 104 105 106
## -1256.142138 3186.081078 -2338.286532 2598.020683 -1155.534239
## 107 108 109 110 111
## 973.330807 2589.388183 -3153.040416 -4720.997284 -847.133718
## 112 113 114 115 116
## 1906.571053 11695.771363 -1243.718361 2667.905792 4261.653302
## 117 118 119 120 121
## 3500.604476 -1102.575143 -4718.239538 -3724.411769 2320.592808
## 122 123 124 125 126
## -1732.433450 1341.176560 8858.652342 845.230552 128.688111
## 127 128 129 130 131
## -2522.742410 2654.533921 7051.453483 1009.752271 -8501.934586
## 132 133 134 135 136
## 1749.279015 4135.182953 -3165.268628 -1419.993675 -853.769080
## 137 138 139 140 141
## -3879.531695 1184.560261 -494.393906 -2912.461292 1719.997048
## 142 143 144 145 146
## -1879.877280 -7827.702705 2042.984443 -3477.137150 2105.417511
## 147 148 149 150 151
## -255.261108 1025.011842 -357.861603 1353.529220 1187.420289
## 152 153 154 155 156
## 3356.934028 -4862.382261 -1173.682818 -3234.791150 5958.491576
## 157 158 159 160 161
## 9746.605833 -3623.824664 -4969.535959 3413.882865 3.171332
## 162 163 164 165 166
## 2504.085903 -6104.052822 -6939.393623 3966.524669 17199.763351
## 167 168 169 170 171
## 3420.579070 -608.237726 -2655.145199 -1311.462811 3383.917432
## 172 173 174 175 176
## -437.340024 -8284.310517 2660.103033 4119.278255 415.952632
## 177 178 179 180 181
## 8540.312700 -9464.861930 -3681.037851 -10951.703129 -11440.284094
## 182 183 184 185 186
## 1041.182907 9096.642777 -1635.284565 5723.295365 6343.579386
## 187 188 189 190 191
## 12938.909423 8197.225419 -4306.491495 2223.830229 10123.858177
## 192 193 194 195 196
## -1899.753942 -2698.887828 -10532.025015 -6603.615936 1000.063932
## 197 198 199 200 201
## -5467.897566 -10024.672294 5165.538188 -3291.995314 -1932.816397
## 202 203 204 205 206
## -1023.006492 6275.664924 9653.507816 335.248354 2680.281476
## 207 208 209 210 211
## 2849.926529 5533.051472 12576.295536 -5958.597567 -11557.211029
## 212 213 214 215 216
## -5910.999505 -10823.469670 -5297.278816 1311.008824 -13228.559747
## 217 218 219 220 221
## 16186.910348 7584.067755 1291.562330 26449.293098 12250.687781
## 222 223 224 225 226
## 7044.582643 13730.221378 -4224.138044 -2040.067117 3486.328909
## 227 228 229 230 231
## 69.775419 2461.841637 8723.392839 5545.414571 -2189.486015
## 232 233 234 235 236
## -2101.659104 9159.962165 -11781.448186 -7537.703579 -8783.991473
## 237 238 239 240 241
## -10332.513891 2859.226683 1129.237859 -8521.856643 -9203.991827
## 242 243 244 245 246
## 8890.581632 -7983.404647 2278.238799 -10515.398562 -4256.336553
## 247 248 249 250 251
## 1222.817484 798.041451 -12525.157926 3447.979788 1858.140945
## 252 253 254 255 256
## 4001.145934 1915.156382 -1385.548572 10913.873968 20636.550266
## 257 258 259 260 261
## 2917.766738 -4554.609292 3834.784603 -1974.026187 3462.742540
## 262 263 264 265 266
## -5129.927785 -11161.663775 -4977.811072 -763.213149 -5429.453862
## 267 268 269 270 271
## 8544.300229 -4531.085816 3944.792309 -2360.280460 4180.208713
## 272 273 274 275 276
## 449.408346 7041.510101 -1687.458289 11752.498062 -4880.353237
## 277 278 279 280 281
## 1438.714179 -661.293725 7564.568671 -5358.407129 -3018.779308
## 282 283 284 285 286
## -11540.412167 -2922.102295 18408.203854 7496.655320 2432.148779
## 287 288 289 290 291
## -933.681925 605.471697 6098.622328 6571.536793 -19094.627748
## 292 293 294 295 296
## -11410.845260 -8363.002046 9444.496722 2828.266299 -1429.683293
## 297 298 299 300 301
## 27155.013575 9750.828822 4566.337125 9178.299948 2501.081565
## 302 303 304 305 306
## -1385.466725 7555.673769 -24647.124881 -3811.674094 -437.237331
## 307 308 309 310 311
## -7225.400648 -4206.459139 2710.543670 -9420.573503 -3431.382286
## 312 313 314 315 316
## -8378.318071 1395.536267 -3328.316017 1876.809942 -4264.135101
## 317 318 319 320 321
## 27271.016545 -1001.363347 3017.278198 10548.475909 5278.622272
## 322 323 324 325 326
## 32059.635399 4706.198130 -21338.533963 1469.018189 791.431136
## 327 328 329 330 331
## -6778.090532 -2019.469027 -33541.184351 749.016602 -2437.528493
## 332 333 334 335 336
## -221.594944 -3297.439479 3963.821044 -575.236050 -7091.748387
## 337 338 339 340 341
## -3235.858068 -2304.821801 -7790.132993 3760.904966 -1482.136581
## 342 343 344 345 346
## -1850.071146 -1106.098781 62.014854 360.760908 -1746.579533
## 347 348 349 350 351
## -9574.666105 -13312.627416 2243.337908 -4408.312920 -3737.939697
## 352 353 354 355 356
## -6056.323812 1684.774200 1300.858976 2654.081592 -3885.295611
## 357 358 359 360 361
## -630.514874 556.455696 6882.453147 113.134542 -206.920470
## 362 363 364 365 366
## 2411.009128 -2934.468164 -1052.262183 -8915.908797 -4765.761308
## 367 368 369 370 371
## -6337.307803 -5054.871690 -7345.100468 4942.297688 268.078784
## 372 373 374 375 376
## 7006.770502 -7784.435326 -2385.353794 -3506.622609 -2577.916434
## 377 378 379 380 381
## -12564.700991 1836.762942 -10718.220630 5643.378672 9248.011399
## 382 383 384 385 386
## 2990.365195 -2553.774492 1453.882027 6581.579934 11216.292474
## 387 388 389 390 391
## -6046.258682 -5586.078254 -361.555854 8357.761629 1572.908512
## 392 393 394 395 396
## 10972.788741 -10172.509457 2522.073156 450.674663 299.972857
## 397 398 399 400 401
## -915.721073 -819.425868 -14738.533836 8339.399981 -1395.718963
## 402 403 404 405 406
## -1578.964770 6783.431487 -8158.876797 -1485.410525 -2711.717427
## 407 408 409 410 411
## -5985.950443 -2999.137234 -4045.564245 -8869.113574 6052.902236
## 412 413 414 415 416
## 1525.307951 -7501.817619 -7793.738090 14145.731951 3666.413887
## 417 418 419 420 421
## 4317.411805 -8235.410789 -4908.951432 -2745.716307 2684.834324
## 422 423 424 425 426
## -14161.407846 -2883.397727 -9184.865424 2959.022555 6899.026529
## 427 428 429 430 431
## 6456.292897 -4142.955515 -4262.123328 -4849.003087 -1900.286070
## 432 433 434 435 436
## -5820.548308 -6716.898235 -6019.324854 -1447.367359 -907.291706
## 437 438 439 440 441
## -5042.102834 2525.230977 4760.051590 -5167.179897 -2254.093843
## 442 443 444 445 446
## 1481.806535 -3946.483497 2735.902310 -6697.373788 -12207.466545
## 447 448 449 450 451
## -4565.023566 9599.520904 -2129.049673 4659.038875 -5991.391597
## 452 453 454 455 456
## -1224.515214 280.750976 2915.802633 -12396.584553 3286.907693
## 457 458 459 460 461
## -6804.843252 6439.770113 2895.690946 2373.449322 -3992.752682
## 462 463 464 465 466
## 1959.469704 -152.066733 1646.273446 -677.004080 3195.709504
## 467 468 469 470 471
## -2808.468841 5647.499963 -7122.856832 -3113.553279 -2341.550548
## 472 473 474 475 476
## -4791.238705 2886.532075 7672.042537 -6175.988768 1350.417968
## 477 478 479 480 481
## -6319.602753 -2960.881055 1904.281042 -13049.015619 -9827.282479
## 482 483 484 485 486
## -1244.781891 -28.143129 -1020.799572 -1405.994425 -9652.463164
## 487 488 489 490 491
## 11055.318460 6141.768576 7296.665466 -5592.202470 5232.508049
## 492 493 494 495 496
## 9135.418354 5860.229633 -13687.290166 -10721.393031 -3554.328162
## 497 498 499 500 501
## -1208.061919 -625.752547 -7728.979247 534.044882 4202.749105
## 502 503 504 505 506
## 5402.195300 529.440195 -54.105652 -7374.737603 461.287878
## 507 508 509 510 511
## -5161.959985 1735.599513 -1404.297746 -8263.922547 -680.900134
## 512 513 514 515 516
## -2756.978179 -665.894596 1250.071606 -9588.181041 -7827.968558
## 517 518 519 520 521
## 24244.330470 9681.998388 5698.068760 -5536.727395 2621.756971
## 522 523 524 525 526
## 16834.151433 11226.889057 -24430.876162 -5238.211232 -3889.349225
## 527 528 529 530 531
## 4431.371302 -515.471102 -11259.212477 4277.877922 13776.529135
## 532 533 534 535 536
## -5164.256782 4207.839569 5373.235095 -1991.150010 -4730.636243
## 537 538 539 540 541
## -7242.602625 -2239.547246 8191.678362 -36.061458 -8303.470791
## 542 543 544 545 546
## 1686.656992 -736.772413 230.953234 -11168.163106 -11157.682872
## 547 548 549 550 551
## 1982.028741 6929.253774 -1424.758735 731.256629 -7833.100580
## 552 553 554 555 556
## 8478.610876 782.680414 -12073.907497 9075.465497 8529.795194
## 557 558 559 560 561
## -64.809750 4689.208631 -3756.271548 13942.227011 21279.247907
## 562 563 564 565 566
## -6763.130212 -9940.227828 6562.186581 -6.862921 3228.313084
## 567 568 569 570 571
## -7609.473528 -17503.206293 6540.734727 6287.842770 1733.147589
## 572 573 574 575 576
## 2925.660418 1589.590717 -2347.414873 14547.463486 -9871.585943
## 577 578 579 580 581
## -6424.271105 8558.527027 2674.107368 -6736.501134 7347.339764
## 582 583 584 585 586
## -3987.255274 -2944.014273 15547.776682 -14711.341901 8276.279009
## 587 588 589 590 591
## -111.379672 -6389.795140 -903.676379 107.581752 -10796.766383
## 592 593 594 595 596
## 1694.660400 -7255.282043 2981.909303 8765.424600 -7637.236889
## 597 598 599 600 601
## 5742.660866 2602.166387 6712.669389 -3357.777878 5996.941687
## 602 603 604 605 606
## -8471.525936 2116.989967 1124.586482 2990.076069 1337.683295
## 607 608 609 610 611
## 237.581904 -5968.336983 7941.553544 -1344.996206 -2726.942390
## 612 613 614 615 616
## -3593.130923 -8352.987024 11864.678260 4797.735318 -9468.703473
## 617 618 619 620 621
## 11508.957809 5891.528010 -5748.450284 26210.598959 -13060.758651
## 622 623 624 625 626
## -6955.767612 3012.274794 -4311.122451 -10725.279994 11202.312108
## 627 628 629 630 631
## -21769.523801 -2474.408205 8618.728283 11044.819036 -1682.417549
## 632 633 634 635 636
## 33161.548317 -6818.564425 5520.243458 5192.503823 -2482.748095
## 637 638 639 640 641
## -5540.575025 -2108.455536 -12586.513249 -2352.283813 -1988.508042
## 642 643 644 645 646
## -2616.870033 -2947.383330 1733.177753 4340.499992 16858.144082
## 647 648 649 650 651
## 18390.627838 665.028012 4577.903173 10394.903796 19910.618936
## 652 653 654 655 656
## 458.223193 -28330.229944 -1500.221218 -2436.995375 1737.520810
## 657 658 659 660 661
## -3321.820799 -10735.602755 1586.589945 4143.599875 -1101.379512
## 662 663 664 665 666
## 12942.025896 1233.619859 1687.417857 -11817.701603 1289.758536
## 667 668 669 670 671
## 1095.317173 -5259.577615 -7487.250614 2010.589712 -3774.759360
## 672 673 674 675 676
## 2619.230580 -3442.403895 -9392.553510 -8341.218292 -2999.028818
## 677 678 679 680 681
## 150.325660 2816.617768 669.491313 -3876.506063 -1852.886537
## 682 683 684 685 686
## -1362.772070 -8288.221517 4618.041712 -2290.236440 -1444.826561
## 687 688 689 690 691
## 540.028658 10800.837704 9768.305500 10520.352759 -9785.571385
## 692 693 694 695 696
## -3646.373536 -3220.742961 5798.678489 -10470.318784 -7970.539707
## 697 698 699 700 701
## -8653.731176 -6301.712662 -4758.965488 3065.483103 -4431.977059
## 702 703 704 705 706
## -1924.580932 4193.958649 31064.810771 9439.016007 23363.537595
## 707 708 709 710 711
## 1593.093077 8246.770500 22849.612745 6488.886336 -18265.387159
## 712 713 714 715 716
## 4783.014907 -5479.632149 -129.701090 452.919944 -17292.061804
## 717 718 719 720 721
## -5279.696632 3321.944057 -3028.363393 -12991.777352 4272.815920
## 722 723 724 725 726
## -5566.525250 734.243867 -3944.394897 -12455.841978 1364.415430
## 727 728 729 730 731
## -1872.777370 -9783.788420 17266.309569 1761.581234 -2737.840579
## 732 733 734 735 736
## 5701.310391 -8647.796255 -735.992403 8125.586435 -15368.999609
## 737 738 739 740 741
## -5920.357043 7400.848080 -4797.093742 150.087724 1815.977623
## 742 743 744 745 746
## -1969.334351 -5181.031139 6401.770864 -6290.053525 22686.648804
## 747 748 749 750 751
## 7803.278248 -1973.363247 -7312.070511 23397.391081 -4319.707123
## 752 753 754 755 756
## 1372.506114 -14444.719155 56094.341558 26888.997841 15062.454403
## 757 758 759 760 761
## -10675.610724 10587.612892 7296.651731 5793.741164 -46402.630654
## 762 763 764 765 766
## -16167.597844 974.496354 -2510.143944 -3450.130216 122852.191740
## 767 768 769 770 771
## 19303.690704 43714.981018 22580.192391 12072.589217 15845.706470
## 772 773 774 775 776
## 25727.032819 -98809.191336 -6746.812831 -35820.663209 1751.260455
## 777 778 779 780 781
## -1215.306712 3409.362051 -7401.996615 -1431.690619 -1931.991854
## 782 783 784 785 786
## 3437.750847 -7142.137834 -2223.041925 3933.349392 2279.094664
## 787 788 789 790 791
## -2745.176182 -4032.932505 1753.969798 2868.607857 -36.100555
## 792 793 794 795 796
## -6687.738694 -5766.117295 -1130.527770 -1246.349712 -7800.519035
## 797 798 799 800 801
## -2336.802771 -3251.161928 -2648.581296 10741.230559 2257.241757
## 802 803 804 805 806
## 7095.655404 2939.125464 -5432.455844 8204.133968 9890.976522
## 807 808 809 810 811
## -10586.195561 -7378.791473 -7486.435379 3025.519310 4213.586798
## 812 813 814 815 816
## -2250.607017 -14145.577743 -4089.592997 6280.721966 8257.347621
## 817 818 819 820 821
## -9653.809123 -7729.293637 -9314.575058 9776.455752 -1202.076010
## 822 823 824 825 826
## -4350.921194 -8425.691477 7896.783139 7934.728981 4994.924375
## 827 828 829 830 831
## -3083.703627 -692.716338 2911.246745 4687.232653 1642.208993
## 832 833 834 835 836
## -6676.396545 2107.544393 -380.064838 2771.564540 4158.211491
## 837 838 839 840 841
## -1119.703707 -9317.753855 5695.564751 -4843.149798 -7558.301518
## 842 843
## 3043.379339 -13023.032649
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17249.34 20098.17 24354.63 24072.43 26427.17 23758.27 24475.00 19704.34
## 10 11 12 13 14 15 16 17
## 19440.75 16781.96 17559.88 14287.37 14338.57 15003.61 16701.17 15020.22
## 18 19 20 21 22 23 24 25
## 16055.49 15428.66 22515.22 21598.70 21078.25 22969.36 22294.95 22947.88
## 26 27 28 29 30 31 32 33
## 24794.91 18719.77 20446.93 28288.81 28346.50 28019.22 25647.25 27050.50
## 34 35 36 37 38 39 40 41
## 30895.78 31244.40 32654.40 30163.56 34139.78 37349.73 34404.12 31213.15
## 42 43 44 45 46 47 48 49
## 30060.26 20634.32 28157.42 30595.31 31685.15 38527.23 38021.51 42686.41
## 50 51 52 53 54 55 56 57
## 46925.14 39618.81 34184.11 29203.92 22344.37 28638.03 25216.68 21512.13
## 58 59 60 61 62 63 64 65
## 25923.05 27183.33 27480.51 27892.57 23761.11 40362.02 42207.05 37450.25
## 66 67 68 69 70 71 72 73
## 41659.63 46578.13 57252.49 55265.12 40499.75 38004.16 41023.81 35320.65
## 74 75 76 77 78 79 80 81
## 30770.25 21468.30 24671.63 20594.98 22682.26 17574.83 19591.43 18817.51
## 82 83 84 85 86 87 88 89
## 17844.67 15912.48 17190.99 20801.14 25221.42 26211.87 26236.84 26853.93
## 90 91 92 93 94 95 96 97
## 30981.40 29811.28 30811.21 28874.52 28073.88 28442.24 28849.05 22422.78
## 98 99 100 101 102 103 104 105
## 25439.57 18466.01 17321.48 15361.07 15661.00 16338.78 20814.00 19896.98
## 106 107 108 109 110 111 112 113
## 23410.11 23199.95 24877.04 27755.47 25252.14 21693.56 21969.14 24616.94
## 114 115 116 117 118 119 120 121
## 35487.72 33679.52 35518.06 38518.11 40475.15 38162.24 32980.27 29319.55
## 122 123 124 125 126 127 128 129
## 31403.58 29682.54 30864.78 38468.91 38111.17 37172.17 34033.89 35816.12
## 130 131 132 133 134 135 136 137
## 41217.10 40657.08 31853.72 33119.25 36310.84 32719.42 31105.77 30190.25
## 138 139 140 141 142 143 144 145
## 26745.30 28160.54 27930.03 25615.00 27640.59 26264.56 19863.02 22895.28
## 146 147 148 149 150 151 152 153
## 20720.73 23699.55 24239.85 25831.15 26013.33 27668.44 28969.92 32003.81
## 154 155 156 157 158 159 160 161
## 27471.40 26733.93 24287.79 30185.25 41644.25 39973.54 37336.97 42360.11
## 162 163 164 165 166 167 168 169
## 43769.49 47187.34 42650.68 37955.19 43383.52 59694.99 61908.38 60321.57
## 170 171 172 173 174 175 176 177
## 57145.46 55543.80 58247.91 57271.45 49559.18 52384.29 56129.05 56165.26
## 178 179 180 181 182 183 184 185
## 63298.15 53795.04 50544.13 41347.57 32882.10 36392.36 46501.57 45957.28
## 186 187 188 189 190 191 192 193
## 51913.42 57661.66 68450.77 73736.63 67427.74 67621.28 74695.61 70369.60
## 194 195 196 197 198 199 200 201
## 65889.88 55127.62 49154.36 50579.47 46171.67 38336.03 44764.42 42990.82
## 202 203 204 205 206 207 208 209
## 42628.58 43107.19 49905.06 58799.32 58428.72 60154.50 61811.23 65604.56
## 210 211 212 213 214 215 216 217
## 75076.45 67154.78 55337.14 49942.90 40934.14 37890.13 41005.56 31020.09
## 218 219 220 221 222 223 224 225
## 48003.22 55328.15 56230.56 79008.88 86508.13 88512.49 96108.14 87053.92
## 226 227 228 229 230 231 232 233
## 81048.96 80630.65 77278.73 76439.75 81179.44 82544.49 76976.80 72187.04
## 234 235 236 237 238 239 240 241
## 77843.88 64484.13 56516.13 48462.23 40069.06 44263.33 46417.29 39864.28
## 242 243 244 245 246 247 248 249
## 33540.28 43828.55 38072.19 42010.11 34269.62 32974.75 36632.10 39457.59
## 250 251 252 253 254 255 256 257
## 30281.88 36223.29 40026.85 45224.56 47944.41 47436.70 57743.45 75250.52
## 258 259 260 261 262 263 264 265
## 75065.47 68372.36 69855.03 66073.69 67520.64 61274.81 50543.38 46568.50
## 266 267 268 269 270 271 272 273
## 46778.03 42882.56 51691.66 47962.64 52111.71 50227.22 54296.88 54593.06
## 274 275 276 277 278 279 280 281
## 60613.89 58246.79 67925.21 61846.57 62056.72 60404.86 66150.98 59877.92
## 282 283 284 285 286 287 288 289
## 56439.84 45986.25 44382.08 61624.06 67157.28 67566.97 64983.10 64069.95
## 290 291 292 293 294 295 296 297
## 68073.18 71985.63 52971.42 43067.86 37075.50 47402.73 50646.40 49759.84
## 298 299 300 301 302 303 304 305
## 73969.89 79918.66 80586.70 85201.78 83399.32 78426.75 81895.55 56780.10
## 306 307 308 309 310 311 312 313
## 53039.09 52718.69 46505.32 43713.17 47318.57 39866.53 38587.89 33146.32
## 314 315 316 317 318 319 320 321
## 36933.03 36113.90 39947.56 37930.84 63731.93 61571.86 63196.38 71199.09
## 322 323 324 325 326 327 328 329
## 73587.79 99084.09 97460.82 73277.12 72074.28 70430.66 62377.75 59498.33
## 330 331 332 333 334 335 336 337
## 29429.41 33119.10 33558.88 35880.15 35220.61 40990.95 42067.18 37312.00
## 338 339 340 341 342 343 344 345
## 36525.96 36652.70 31968.95 37971.42 38635.21 38893.81 39770.13 41557.10
## 346 347 348 349 350 351 352 353
## 43380.15 43131.67 36072.20 26634.52 31982.31 30842.65 30432.47 28047.51
## 354 355 356 357 358 359 360 361
## 32729.14 36485.63 40951.87 39139.80 40400.83 42540.55 49940.15 50491.06
## 362 363 364 365 366 367 368 369
## 50692.85 53157.47 50639.41 50083.62 42724.48 39919.59 36094.30 33871.67
## 370 371 372 373 374 375 376 377
## 29927.13 37219.35 39507.66 47397.86 41365.93 40812.77 39349.20 38881.70
## 378 379 380 381 382 383 384 385
## 29743.95 34344.79 27392.34 35616.56 45955.78 49523.35 47795.69 49788.56
## 386 387 388 389 390 391 392 393
## 56012.42 65503.54 58710.79 53175.70 52904.24 60288.23 60811.93 69485.80
## 394 395 396 397 398 399 400 401
## 58584.93 60152.75 59712.60 59196.15 57682.14 56442.96 43193.60 51784.43
## 402 403 404 405 406 407 408 409
## 50784.25 49749.85 56155.02 48692.98 48003.72 46329.38 42003.99 40833.99
## 410 411 412 413 414 415 416 417
## 38896.69 32987.24 40864.83 43792.96 38462.02 33547.27 48428.01 52275.16
## 418 419 420 421 422 423 424 425
## 56206.84 48671.38 44992.43 43667.59 47256.26 35668.25 35397.29 29652.55
## 426 427 428 429 430 431 432 433
## 35245.83 43578.56 50474.96 47238.41 44305.29 41228.57 41116.69 37592.33
## 434 435 436 437 438 439 440 441
## 33728.32 30960.65 32537.72 34388.25 32391.63 37260.81 43470.18 40220.52
## 442 443 444 445 446 447 448 449
## 39926.34 42934.63 40819.38 44811.37 40055.32 31082.02 29918.76 41282.76
## 450 451 452 453 454 455 456 457
## 40964.10 46618.82 42252.23 42602.11 44223.63 47944.16 37812.09 42664.41
## 458 459 460 461 462 463 464 465
## 38084.80 45658.59 49180.84 51803.04 48530.53 50872.78 51074.44 52822.58
## 466 467 468 469 470 471 472 473
## 52319.86 55265.47 52592.07 57646.43 50902.12 48511.55 47096.81 43719.04
## 474 475 476 477 478 479 480 481
## 47477.53 54945.56 49369.01 51073.32 45858.88 44236.86 47071.59 36479.14
## 482 483 484 485 486 487 488 489
## 30036.64 31907.14 34605.51 36096.42 37062.89 30699.68 43237.80 49902.19
## 490 491 492 493 494 495 496 497
## 56736.77 51444.92 56281.01 63919.48 67733.29 53980.96 44552.90 42576.63
## 498 499 500 501 502 503 504 505
## 42900.04 43691.69 38174.96 40575.39 45880.23 51565.42 52275.53 52386.17
## 506 507 508 509 510 511 512 513
## 46084.14 47424.96 43681.83 46439.01 46104.49 39816.33 40948.12 40122.75
## 514 515 516 517 518 519 520 521
## 41229.07 43870.75 36706.40 31982.81 55887.43 64053.22 67708.44 61083.39
## 522 523 524 525 526 527 528 529
## 62423.71 76017.83 82998.88 57933.50 52800.35 49492.63 53874.33 53380.36
## 530 531 532 533 534 535 536 537
## 43557.84 48552.76 61221.11 55738.59 59138.34 63128.58 60179.35 55207.03
## 538 539 540 541 542 543 544 545
## 48665.26 47320.32 55262.35 55012.61 47568.06 49793.06 49619.62 50313.88
## 546 547 548 549 550 551 552 553
## 40957.11 32787.83 37132.32 45253.90 45050.74 46757.67 40763.82 49782.32
## 554 555 556 557 558 559 560 561
## 50938.34 40711.25 50258.06 58125.67 57490.22 61090.13 56854.77 68622.47
## 562 563 564 565 566 567 568 569
## 85321.27 75406.23 63962.81 68384.72 66507.97 67695.33 59260.21 43239.55
## 570 571 572 573 574 575 576 577
## 50252.44 56161.14 57344.63 59421.41 60068.84 57193.54 69447.59 58814.56
## 578 579 580 581 582 583 584 585
## 52533.76 60139.89 61644.79 54734.66 61004.97 56578.44 53621.22 67199.48
## 586 587 588 589 590 591 592 593
## 52619.29 59967.95 59059.80 52778.25 52082.99 52359.19 43069.48 45868.00
## 594 595 596 597 598 599 600 601
## 40491.23 44739.58 53508.09 46835.34 52697.83 55077.04 60749.49 56905.34
## 602 603 604 605 606 607 608 609
## 61721.95 53285.58 55166.70 55943.50 58253.03 58827.42 58367.91 52541.88
## 610 611 612 613 614 615 616 617
## 59607.71 57666.66 54762.13 51466.27 44425.04 55942.12 59831.85 50761.90
## 618 619 620 621 622 623 624 625
## 61170.04 65357.45 58843.40 81084.04 66198.05 58522.87 60526.98 55877.57
## 626 627 628 629 630 631 632 633
## 46207.26 56920.95 37465.84 37325.99 46899.90 57388.70 55432.17 84177.99
## 634 635 636 637 638 639 640 641
## 74358.47 76560.50 78198.75 72922.00 65637.03 62269.37 50167.28 48534.65
## 642 643 644 645 646 647 648 649
## 47425.58 45906.95 44290.68 46969.07 51589.14 66568.66 81001.26 78122.95
## 650 651 652 653 654 655 656 657
## 79027.24 84902.10 98354.49 93110.09 63363.08 60813.42 57766.05 58751.25
## 658 659 660 661 662 663 664 665
## 55190.17 45597.41 47983.11 52303.38 51495.12 63063.52 62941.15 63230.84
## 666 667 668 669 670 671 672 673
## 51679.67 53039.97 54059.01 49395.11 43371.41 46408.05 44005.48 47494.26
## 674 675 676 677 678 679 680 681
## 45245.41 38078.93 32733.89 32731.39 35481.95 40216.65 42478.36 40481.74
## 682 683 684 685 686 687 688 689
## 40505.34 40954.36 35293.53 41626.52 41123.68 41423.11 43419.73 54133.55
## 690 691 692 693 694 695 696 697
## 62595.65 70649.43 59940.23 55945.74 52826.32 57983.32 48270.68 41966.16
## 698 699 700 701 702 703 704 705
## 35858.43 32575.68 31054.80 36564.55 34827.15 35500.18 41436.47 70112.13
## 706 707 708 709 710 711 712 713
## 76274.18 93831.19 90148.37 92745.10 107778.69 106618.67 83967.84 84315.35
## 714 715 716 717 718 719 720 721
## 75648.84 72749.94 70725.35 53445.41 48841.20 52335.22 49838.63 38947.76
## 722 723 724 725 726 727 728 729
## 44518.81 40788.04 43034.39 40908.41 31610.58 35563.49 36189.07 29821.12
## 730 731 732 733 734 735 736 737
## 47898.70 50147.55 48180.40 53837.37 46239.85 46514.56 54500.29 40944.50
## 738 739 740 741 742 743 744 745
## 37354.58 45860.38 42633.20 44136.59 46906.76 46019.46 42436.66 49429.20
## 746 747 748 749 750 751 752 753
## 44447.64 65421.01 70744.08 66851.36 58782.47 78571.85 71642.49 70561.15
## 754 755 756 757 758 759 760 761
## 55790.66 104536.15 121615.55 126206.90 107723.24 110152.78 109399.83 107428.06
## 762 763 764 765 766 767 768 769
## 60081.45 45124.79 47035.00 45658.84 43634.38 152261.60 156700.73 181917.95
## 770 771 772 773 774 775 776 777
## 185486.27 179420.86 177417.25 184302.91 81468.38 72052.81 38410.45 41845.16
## 778 779 780 781 782 783 784 785
## 42254.35 46654.28 41050.26 41370.42 41212.96 45768.85 40503.47 40200.79
## 786 787 788 789 790 791 792 793
## 45317.33 48343.60 46597.22 43945.17 46685.25 50054.53 50460.60 45001.55
## 794 795 796 797 798 799 800 801
## 41035.53 41620.78 42031.09 36660.95 36742.73 36015.01 35905.63 47513.62
## 802 803 804 805 806 807 808 809
## 50244.20 56860.02 59009.60 53571.15 60736.88 68474.62 57339.51 50410.15
## 810 811 812 813 814 815 816 817
## 44259.34 48071.27 52441.61 50611.43 38614.74 36918.42 44500.08 52854.67
## 818 819 820 821 822 823 824 825
## 44501.58 38882.58 32585.54 43768.36 43946.92 41350.69 35519.79 44690.13
## 826 827 828 829 830 831 832 833
## 52738.79 57204.28 54046.14 53375.61 55939.62 59733.08 60387.25 53688.03
## 834 835 836 837 838 839 840 841
## 55510.21 54928.58 57174.93 60350.42 58512.75 49740.86 55196.29 50753.16
## 842 843
## 44496.33 48294.03
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8098
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.057179 0.7816901 3.921532
## t2* 2700.830375 167.2826323 895.204321
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.059395 5.01345 13.1873
## 2 lag_depvar 1630.744280 2739.57659 4537.0444
Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jun 16 01:03:48 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jun 16 01:03:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jun 16 01:04:07 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jun 16 01:04:16 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jun 16 01:04:25 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jun 16 01:04:35 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jun 16 01:04:44 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jun 16 01:04:54 2025
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## =-=-=-=-= Iteration 16000 Mon Jun 16 01:05:03 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jun 16 01:05:13 2025
## =-=-=-=-=
#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 11.4882 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 250.2842 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.4514 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.6380 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 33.1500 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 20.8978 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 17.5960 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 391.5056 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2746, min uf: 26799.01, min date: 2018-01-01
#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
## Warning: Removed 2746 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
##
## invoke
## The following object is masked from 'package:data.table':
##
## :=
model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-07-09 00:04:58 sería de: 26.580 pesos// Percentil 95% más alto proyectado: 35.072,1
Según TimeGPT: La proyección de la UF a 298 días más 2026-05-03 sería de: 40.188,82 pesos// Percentil 80% más alto proyectado: 40.557,47 pesos// Percentil 95% más alto proyectado: 41.617,67
Según prophet: La proyección de la UF a 298 días más 2026-05-03 sería de: 41.974 pesos// Percentil 95% más alto proyectado: 51.362
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26197.33 | 26323.55 |
| Lo.80 | 26329.23 | 26488.13 |
| Point.Forecast | 26580.21 | 26799.01 |
| Hi.80 | 31419.69 | 32165.71 |
| Hi.95 | 34328.67 | 35006.68 |
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t
Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4299 1039.5096
## s.e. 0.1057 38.3598
##
## sigma^2 = 37907: log likelihood = -507.56
## AIC=1021.12 AICc=1021.45 BIC=1028.11
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4100 701.0985 10.4065
## s.e. 0.1073 320.5008 9.7774
##
## sigma^2 = 37890: log likelihood = -507.01
## AIC=1022.03 AICc=1022.59 BIC=1031.35
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 640.1200 | 616.8435 | 607.9829 |
| Lo.80 | 784.9037 | 763.1390 | 701.1458 |
| Point.Forecast | 1058.4066 | 1039.4978 | 917.6171 |
| Hi.80 | 1331.9094 | 1315.8566 | 1200.5224 |
| Hi.95 | 1476.6931 | 1462.1521 | 1383.8652 |
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Sys.getenv("R_LIBS_USER")
## [1] "C:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.4
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.1.0
## [4] httr2_1.1.2 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.18.0 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.3.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.2 askpass_1.2.1 pkgbuild_1.4.8
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 data.tree_1.1.0 tokenizers_0.3.0
## [31] listenv_0.9.1 anytime_0.3.11 performance_0.14.0
## [34] spatial_7.3-17 parallelly_1.45.0 codetools_0.2-20
## [37] xml2_1.3.8 tidyselect_1.2.1 ggeffects_2.2.1
## [40] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [43] matrixStats_1.5.0 stats4_4.4.0 jsonlite_2.0.0
## [46] ellipsis_0.3.2 Formula_1.2-5 iterators_1.0.14
## [49] systemfonts_1.2.3 foreach_1.5.2 tools_4.4.0
## [52] glue_1.8.0 xfun_0.52 TTR_0.24.4
## [55] ggfortify_0.4.17 loo_2.8.0 withr_3.0.2
## [58] timeSeries_4041.111 fastmap_1.2.0 boot_1.3-30
## [61] openssl_2.3.3 caTools_1.18.3 digest_0.6.37
## [64] timechange_0.3.0 R6_2.6.1 lfe_3.1.1
## [67] colorspace_2.1-1 networkD3_0.4.1 gtools_3.9.5
## [70] generics_0.1.4 htmlwidgets_1.6.4 ggstats_0.9.0
## [73] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110
## [76] lmtest_0.9-40 selectr_0.4-2 janeaustenr_1.0.0
## [79] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [82] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [85] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [88] curl_6.3.0 cachem_1.1.0 sjlabelled_1.2.0
## [91] KernSmooth_2.23-22 parallel_4.4.0 fBasics_4041.97
## [94] pillar_1.10.2 vctrs_0.6.5 gplots_3.2.0
## [97] slam_0.1-55 car_3.1-3 dbplyr_2.5.0
## [100] xtable_1.8-4 evaluate_1.0.3 mvtnorm_1.3-3
## [103] cli_3.6.5 compiler_4.4.0 crayon_1.5.3
## [106] rngtools_1.5.2 future.apply_1.20.0 labeling_0.4.3
## [109] sjmisc_2.8.10 rstan_2.32.7 QuickJSR_1.7.0
## [112] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.58.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.27 bit_4.6.0
#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))